摘要
针对复杂、不确定、非均匀采样数据的非线性系统,提出一种基于矩阵奇异值分解(SVD)的模型结构辨识和参数估计的建模方法.首先,利用矩阵奇异值(SVD)分解算法分析各局部模型与奇异值、积累贡献率的关系,确定模糊模型的规则数,从而实现模型的结构优化;然后,为了克服递推最小二乘出现的误差积累、传递现象,采用奇异值分解的递推最小二乘估计模型的结论参数;最后,通过仿真实例验证所提出算法的有效性.
A modeling method based on matrix singular value decomposition(SVD)for model structure identification and parameter estimation is proposed for the complex,uncertain and non-uniformly sampled data nonlinear systems.Firstly,the matrix singular value decomposition method is used to analyze the relationship among the local model and the accumulation contribution rates,and the rule number of the fuzzy model is determined.Thus,the structure optimization of the model is realized.Then,in order to overcome the error accumulation and transfer of recursive least squares,the conclusion parameters of the recursive least square estimation algorithm with singular value decomposition is adopted.Finally,a simulation example is given to illustrate the effectiveness of the proposed algorithm.
作者
王宏伟
谢丽蓉
WANG Hong-wei;XIE Li-rong(School of Electrical Engineering,Xinjiang University,Urumqi 830036,China;School of Control Science and Control Engineering,Dalian University of Technology,Dalian 116024,China)
出处
《控制与决策》
EI
CSCD
北大核心
2020年第3期757-762,共6页
Control and Decision
基金
国家自然科学基金项目(61863034,51667021)
国家国际科技合作专项项目(2013DFG61520)
科技援疆计划项目(2018E02072).
关键词
非均匀采样系统
模糊模型
奇异值分解
递推最小二乘
结构辨识
nonuniformly sampled system
fuzzy model
singular value decomposition
recursive least square
structure identification
